Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations1000
Missing cells91
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory435.0 KiB
Average record size in memory445.4 B

Variable types

Text1
Numeric9
Categorical4
Boolean2

Alerts

exam_score is highly overall correlated with study_hours_per_dayHigh correlation
study_hours_per_day is highly overall correlated with exam_scoreHigh correlation
parental_education_level has 91 (9.1%) missing valuesMissing
student_id has unique valuesUnique
study_hours_per_day has 13 (1.3%) zerosZeros
social_media_hours has 21 (2.1%) zerosZeros
netflix_hours has 59 (5.9%) zerosZeros
exercise_frequency has 144 (14.4%) zerosZeros

Reproduction

Analysis started2025-11-08 14:10:32.736031
Analysis finished2025-11-08 14:10:37.613704
Duration4.88 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

student_id
Text

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size52.9 KiB
2025-11-08T15:10:37.724461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st rowS1000
2nd rowS1001
3rd rowS1002
4th rowS1003
5th rowS1004
ValueCountFrequency (%)
s10151
 
0.1%
s19991
 
0.1%
s10001
 
0.1%
s10011
 
0.1%
s10021
 
0.1%
s10031
 
0.1%
s10041
 
0.1%
s10051
 
0.1%
s19681
 
0.1%
s19691
 
0.1%
Other values (990)990
99.0%
2025-11-08T15:10:37.879644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11300
26.0%
S1000
20.0%
9300
 
6.0%
0300
 
6.0%
2300
 
6.0%
3300
 
6.0%
4300
 
6.0%
5300
 
6.0%
6300
 
6.0%
7300
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11300
26.0%
S1000
20.0%
9300
 
6.0%
0300
 
6.0%
2300
 
6.0%
3300
 
6.0%
4300
 
6.0%
5300
 
6.0%
6300
 
6.0%
7300
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11300
26.0%
S1000
20.0%
9300
 
6.0%
0300
 
6.0%
2300
 
6.0%
3300
 
6.0%
4300
 
6.0%
5300
 
6.0%
6300
 
6.0%
7300
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11300
26.0%
S1000
20.0%
9300
 
6.0%
0300
 
6.0%
2300
 
6.0%
3300
 
6.0%
4300
 
6.0%
5300
 
6.0%
6300
 
6.0%
7300
 
6.0%

age
Real number (ℝ)

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.498
Minimum17
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-11-08T15:10:37.920723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile17
Q118.75
median20
Q323
95-th percentile24
Maximum24
Range7
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation2.3080995
Coefficient of variation (CV)0.11260121
Kurtosis-1.2189938
Mean20.498
Median Absolute Deviation (MAD)2
Skewness0.0084371397
Sum20498
Variance5.3273233
MonotonicityNot monotonic
2025-11-08T15:10:37.950911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
20146
14.6%
24134
13.4%
17133
13.3%
21125
12.5%
23119
11.9%
18117
11.7%
19113
11.3%
22113
11.3%
ValueCountFrequency (%)
17133
13.3%
18117
11.7%
19113
11.3%
20146
14.6%
21125
12.5%
22113
11.3%
23119
11.9%
24134
13.4%
ValueCountFrequency (%)
24134
13.4%
23119
11.9%
22113
11.3%
21125
12.5%
20146
14.6%
19113
11.3%
18117
11.7%
17133
13.3%

gender
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size52.9 KiB
Female
481 
Male
477 
Other
 
42

Length

Max length6
Median length5
Mean length5.004
Min length4

Characters and Unicode

Total characters5004
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female481
48.1%
Male477
47.7%
Other42
 
4.2%

Length

2025-11-08T15:10:37.992658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T15:10:38.019274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female481
48.1%
male477
47.7%
other42
 
4.2%

Most occurring characters

ValueCountFrequency (%)
e1481
29.6%
a958
19.1%
l958
19.1%
F481
 
9.6%
m481
 
9.6%
M477
 
9.5%
O42
 
0.8%
t42
 
0.8%
h42
 
0.8%
r42
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)5004
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1481
29.6%
a958
19.1%
l958
19.1%
F481
 
9.6%
m481
 
9.6%
M477
 
9.5%
O42
 
0.8%
t42
 
0.8%
h42
 
0.8%
r42
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5004
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1481
29.6%
a958
19.1%
l958
19.1%
F481
 
9.6%
m481
 
9.6%
M477
 
9.5%
O42
 
0.8%
t42
 
0.8%
h42
 
0.8%
r42
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5004
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1481
29.6%
a958
19.1%
l958
19.1%
F481
 
9.6%
m481
 
9.6%
M477
 
9.5%
O42
 
0.8%
t42
 
0.8%
h42
 
0.8%
r42
 
0.8%

study_hours_per_day
Real number (ℝ)

High correlation  Zeros 

Distinct78
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5501
Minimum0
Maximum8.3
Zeros13
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-11-08T15:10:38.054491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.1
Q12.6
median3.5
Q34.5
95-th percentile6
Maximum8.3
Range8.3
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation1.4688899
Coefficient of variation (CV)0.41376016
Kurtosis-0.055651868
Mean3.5501
Median Absolute Deviation (MAD)1
Skewness0.054253101
Sum3550.1
Variance2.1576376
MonotonicityNot monotonic
2025-11-08T15:10:38.098375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.538
 
3.8%
3.236
 
3.6%
4.335
 
3.5%
3.334
 
3.4%
3.831
 
3.1%
4.126
 
2.6%
3.626
 
2.6%
325
 
2.5%
3.925
 
2.5%
2.524
 
2.4%
Other values (68)700
70.0%
ValueCountFrequency (%)
013
1.3%
0.11
 
0.1%
0.21
 
0.1%
0.34
 
0.4%
0.54
 
0.4%
0.61
 
0.1%
0.75
 
0.5%
0.87
0.7%
0.93
 
0.3%
15
 
0.5%
ValueCountFrequency (%)
8.31
 
0.1%
8.21
 
0.1%
7.61
 
0.1%
7.51
 
0.1%
7.43
0.3%
7.31
 
0.1%
7.22
0.2%
7.11
 
0.1%
71
 
0.1%
6.91
 
0.1%

social_media_hours
Real number (ℝ)

Zeros 

Distinct60
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5055
Minimum0
Maximum7.2
Zeros21
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-11-08T15:10:38.142415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.595
Q11.7
median2.5
Q33.3
95-th percentile4.5
Maximum7.2
Range7.2
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.1724224
Coefficient of variation (CV)0.4679395
Kurtosis-0.094082029
Mean2.5055
Median Absolute Deviation (MAD)0.8
Skewness0.1198052
Sum2505.5
Variance1.3745743
MonotonicityNot monotonic
2025-11-08T15:10:38.188365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.138
 
3.8%
3.236
 
3.6%
2.936
 
3.6%
2.235
 
3.5%
2.135
 
3.5%
334
 
3.4%
2.434
 
3.4%
2.332
 
3.2%
1.932
 
3.2%
1.831
 
3.1%
Other values (50)657
65.7%
ValueCountFrequency (%)
021
2.1%
0.13
 
0.3%
0.28
 
0.8%
0.36
 
0.6%
0.46
 
0.6%
0.56
 
0.6%
0.68
 
0.8%
0.710
1.0%
0.810
1.0%
0.920
2.0%
ValueCountFrequency (%)
7.21
 
0.1%
6.21
 
0.1%
6.11
 
0.1%
61
 
0.1%
5.71
 
0.1%
5.61
 
0.1%
5.42
0.2%
5.31
 
0.1%
5.21
 
0.1%
54
0.4%

netflix_hours
Real number (ℝ)

Zeros 

Distinct51
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8197
Minimum0
Maximum5.4
Zeros59
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-11-08T15:10:38.233693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1.8
Q32.525
95-th percentile3.6
Maximum5.4
Range5.4
Interquartile range (IQR)1.525

Descriptive statistics

Standard deviation1.0751176
Coefficient of variation (CV)0.59082133
Kurtosis-0.43285846
Mean1.8197
Median Absolute Deviation (MAD)0.8
Skewness0.2371544
Sum1819.7
Variance1.1558778
MonotonicityNot monotonic
2025-11-08T15:10:38.278747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
059
 
5.9%
248
 
4.8%
1.741
 
4.1%
1.440
 
4.0%
1.639
 
3.9%
2.337
 
3.7%
2.235
 
3.5%
2.434
 
3.4%
0.933
 
3.3%
2.133
 
3.3%
Other values (41)601
60.1%
ValueCountFrequency (%)
059
5.9%
0.112
 
1.2%
0.211
 
1.1%
0.317
 
1.7%
0.417
 
1.7%
0.521
 
2.1%
0.619
 
1.9%
0.728
2.8%
0.821
 
2.1%
0.933
3.3%
ValueCountFrequency (%)
5.41
 
0.1%
5.31
 
0.1%
51
 
0.1%
4.91
 
0.1%
4.61
 
0.1%
4.51
 
0.1%
4.41
 
0.1%
4.35
0.5%
4.24
0.4%
4.17
0.7%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
785 
True
215 
ValueCountFrequency (%)
False785
78.5%
True215
 
21.5%
2025-11-08T15:10:38.309621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

attendance_percentage
Real number (ℝ)

Distinct320
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.1317
Minimum56
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-11-08T15:10:38.341374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum56
5-th percentile67.595
Q178
median84.4
Q391.025
95-th percentile100
Maximum100
Range44
Interquartile range (IQR)13.025

Descriptive statistics

Standard deviation9.3992463
Coefficient of variation (CV)0.11172063
Kurtosis-0.3907113
Mean84.1317
Median Absolute Deviation (MAD)6.5
Skewness-0.23781043
Sum84131.7
Variance88.345831
MonotonicityNot monotonic
2025-11-08T15:10:38.387204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10066
 
6.6%
85.812
 
1.2%
85.38
 
0.8%
92.37
 
0.7%
79.97
 
0.7%
85.27
 
0.7%
84.87
 
0.7%
81.77
 
0.7%
83.27
 
0.7%
82.46
 
0.6%
Other values (310)866
86.6%
ValueCountFrequency (%)
561
0.1%
56.71
0.1%
57.61
0.1%
59.51
0.1%
59.71
0.1%
59.81
0.1%
59.91
0.1%
60.61
0.1%
611
0.1%
61.21
0.1%
ValueCountFrequency (%)
10066
6.6%
99.81
 
0.1%
99.52
 
0.2%
99.42
 
0.2%
99.11
 
0.1%
991
 
0.1%
98.92
 
0.2%
98.82
 
0.2%
98.64
 
0.4%
98.52
 
0.2%

sleep_hours
Real number (ℝ)

Distinct68
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4701
Minimum3.2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-11-08T15:10:38.431317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.2
5-th percentile4.595
Q15.6
median6.5
Q37.3
95-th percentile8.5
Maximum10
Range6.8
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.2263768
Coefficient of variation (CV)0.18954526
Kurtosis-0.21430896
Mean6.4701
Median Absolute Deviation (MAD)0.9
Skewness0.091483972
Sum6470.1
Variance1.504
MonotonicityNot monotonic
2025-11-08T15:10:38.478058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.540
 
4.0%
6.136
 
3.6%
6.235
 
3.5%
6.734
 
3.4%
5.533
 
3.3%
7.133
 
3.3%
732
 
3.2%
6.632
 
3.2%
6.331
 
3.1%
5.830
 
3.0%
Other values (58)664
66.4%
ValueCountFrequency (%)
3.21
 
0.1%
3.33
0.3%
3.41
 
0.1%
3.52
 
0.2%
3.63
0.3%
3.72
 
0.2%
3.83
0.3%
3.92
 
0.2%
45
0.5%
4.16
0.6%
ValueCountFrequency (%)
102
 
0.2%
9.81
 
0.1%
9.72
 
0.2%
9.61
 
0.1%
9.53
0.3%
9.42
 
0.2%
9.34
0.4%
9.21
 
0.1%
9.16
0.6%
94
0.4%

diet_quality
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size51.9 KiB
Fair
437 
Good
378 
Poor
185 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4000
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFair
2nd rowGood
3rd rowPoor
4th rowPoor
5th rowFair

Common Values

ValueCountFrequency (%)
Fair437
43.7%
Good378
37.8%
Poor185
18.5%

Length

2025-11-08T15:10:38.656175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T15:10:38.680378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fair437
43.7%
good378
37.8%
poor185
18.5%

Most occurring characters

ValueCountFrequency (%)
o1126
28.1%
r622
15.6%
a437
 
10.9%
F437
 
10.9%
i437
 
10.9%
G378
 
9.4%
d378
 
9.4%
P185
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o1126
28.1%
r622
15.6%
a437
 
10.9%
F437
 
10.9%
i437
 
10.9%
G378
 
9.4%
d378
 
9.4%
P185
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o1126
28.1%
r622
15.6%
a437
 
10.9%
F437
 
10.9%
i437
 
10.9%
G378
 
9.4%
d378
 
9.4%
P185
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o1126
28.1%
r622
15.6%
a437
 
10.9%
F437
 
10.9%
i437
 
10.9%
G378
 
9.4%
d378
 
9.4%
P185
 
4.6%

exercise_frequency
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.042
Minimum0
Maximum6
Zeros144
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-11-08T15:10:38.705700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.025423
Coefficient of variation (CV)0.66581953
Kurtosis-1.2765261
Mean3.042
Median Absolute Deviation (MAD)2
Skewness-0.031922972
Sum3042
Variance4.1023383
MonotonicityNot monotonic
2025-11-08T15:10:38.733395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3153
15.3%
6152
15.2%
5149
14.9%
1146
14.6%
0144
14.4%
4134
13.4%
2122
12.2%
ValueCountFrequency (%)
0144
14.4%
1146
14.6%
2122
12.2%
3153
15.3%
4134
13.4%
5149
14.9%
6152
15.2%
ValueCountFrequency (%)
6152
15.2%
5149
14.9%
4134
13.4%
3153
15.3%
2122
12.2%
1146
14.6%
0144
14.4%

parental_education_level
Categorical

Missing 

Distinct3
Distinct (%)0.3%
Missing91
Missing (%)9.1%
Memory size56.5 KiB
High School
392 
Bachelor
350 
Master
167 

Length

Max length11
Median length8
Mean length8.9262926
Min length6

Characters and Unicode

Total characters8114
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaster
2nd rowHigh School
3rd rowHigh School
4th rowMaster
5th rowMaster

Common Values

ValueCountFrequency (%)
High School392
39.2%
Bachelor350
35.0%
Master167
16.7%
(Missing)91
 
9.1%

Length

2025-11-08T15:10:38.768632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T15:10:38.793343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
high392
30.1%
school392
30.1%
bachelor350
26.9%
master167
12.8%

Most occurring characters

ValueCountFrequency (%)
h1134
14.0%
o1134
14.0%
c742
9.1%
l742
9.1%
r517
 
6.4%
e517
 
6.4%
a517
 
6.4%
H392
 
4.8%
392
 
4.8%
S392
 
4.8%
Other values (6)1635
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)8114
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h1134
14.0%
o1134
14.0%
c742
9.1%
l742
9.1%
r517
 
6.4%
e517
 
6.4%
a517
 
6.4%
H392
 
4.8%
392
 
4.8%
S392
 
4.8%
Other values (6)1635
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8114
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h1134
14.0%
o1134
14.0%
c742
9.1%
l742
9.1%
r517
 
6.4%
e517
 
6.4%
a517
 
6.4%
H392
 
4.8%
392
 
4.8%
S392
 
4.8%
Other values (6)1635
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8114
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h1134
14.0%
o1134
14.0%
c742
9.1%
l742
9.1%
r517
 
6.4%
e517
 
6.4%
a517
 
6.4%
H392
 
4.8%
392
 
4.8%
S392
 
4.8%
Other values (6)1635
20.2%

internet_quality
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size53.0 KiB
Good
447 
Average
391 
Poor
162 

Length

Max length7
Median length4
Mean length5.173
Min length4

Characters and Unicode

Total characters5173
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAverage
2nd rowAverage
3rd rowPoor
4th rowGood
5th rowGood

Common Values

ValueCountFrequency (%)
Good447
44.7%
Average391
39.1%
Poor162
 
16.2%

Length

2025-11-08T15:10:38.826907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T15:10:38.853446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
good447
44.7%
average391
39.1%
poor162
 
16.2%

Most occurring characters

ValueCountFrequency (%)
o1218
23.5%
e782
15.1%
r553
10.7%
G447
 
8.6%
d447
 
8.6%
A391
 
7.6%
v391
 
7.6%
a391
 
7.6%
g391
 
7.6%
P162
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)5173
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o1218
23.5%
e782
15.1%
r553
10.7%
G447
 
8.6%
d447
 
8.6%
A391
 
7.6%
v391
 
7.6%
a391
 
7.6%
g391
 
7.6%
P162
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5173
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o1218
23.5%
e782
15.1%
r553
10.7%
G447
 
8.6%
d447
 
8.6%
A391
 
7.6%
v391
 
7.6%
a391
 
7.6%
g391
 
7.6%
P162
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5173
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o1218
23.5%
e782
15.1%
r553
10.7%
G447
 
8.6%
d447
 
8.6%
A391
 
7.6%
v391
 
7.6%
a391
 
7.6%
g391
 
7.6%
P162
 
3.1%

mental_health_rating
Real number (ℝ)

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.438
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-11-08T15:10:38.879737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8475014
Coefficient of variation (CV)0.52363027
Kurtosis-1.1886019
Mean5.438
Median Absolute Deviation (MAD)2
Skewness0.0378107
Sum5438
Variance8.1082643
MonotonicityNot monotonic
2025-11-08T15:10:38.909140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4110
11.0%
6108
10.8%
3105
10.5%
8105
10.5%
1102
10.2%
1099
9.9%
599
9.9%
294
9.4%
791
9.1%
987
8.7%
ValueCountFrequency (%)
1102
10.2%
294
9.4%
3105
10.5%
4110
11.0%
599
9.9%
6108
10.8%
791
9.1%
8105
10.5%
987
8.7%
1099
9.9%
ValueCountFrequency (%)
1099
9.9%
987
8.7%
8105
10.5%
791
9.1%
6108
10.8%
599
9.9%
4110
11.0%
3105
10.5%
294
9.4%
1102
10.2%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
682 
True
318 
ValueCountFrequency (%)
False682
68.2%
True318
31.8%
2025-11-08T15:10:38.931554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

exam_score
Real number (ℝ)

High correlation 

Distinct480
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.6015
Minimum18.4
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-11-08T15:10:38.963558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18.4
5-th percentile41.7
Q158.475
median70.5
Q381.325
95-th percentile99.305
Maximum100
Range81.6
Interquartile range (IQR)22.85

Descriptive statistics

Standard deviation16.888564
Coefficient of variation (CV)0.24264655
Kurtosis-0.41990756
Mean69.6015
Median Absolute Deviation (MAD)11.6
Skewness-0.15635066
Sum69601.5
Variance285.22359
MonotonicityNot monotonic
2025-11-08T15:10:39.011588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10048
 
4.8%
70.77
 
0.7%
80.97
 
0.7%
65.67
 
0.7%
747
 
0.7%
76.16
 
0.6%
70.96
 
0.6%
716
 
0.6%
75.46
 
0.6%
66.75
 
0.5%
Other values (470)895
89.5%
ValueCountFrequency (%)
18.41
0.1%
23.11
0.1%
26.21
0.1%
26.71
0.1%
26.82
0.2%
27.61
0.1%
281
0.1%
29.51
0.1%
29.71
0.1%
29.91
0.1%
ValueCountFrequency (%)
10048
4.8%
99.91
 
0.1%
99.41
 
0.1%
99.31
 
0.1%
991
 
0.1%
98.82
 
0.2%
98.73
 
0.3%
98.51
 
0.1%
98.41
 
0.1%
98.31
 
0.1%

Interactions

2025-11-08T15:10:37.127390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:33.393374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:34.044575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:34.446970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:35.210069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:35.577982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:35.934136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.304337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.657220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:37.169647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:33.482358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:34.091823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:34.497323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:35.254808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:35.616692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:35.975700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.345231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.695836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:37.209374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:33.570448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:34.131958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:34.540809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:35.297051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:35.654890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.015173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.382881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.731707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:37.252246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:33.662381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:34.178220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:34.589284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:35.340443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:35.694447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.057334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.423138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.771856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:37.292227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:33.751182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:34.220446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:34.631225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:35.383138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:35.733048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.100221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.460639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.808324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:37.332931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:33.835564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:34.265804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:34.681012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:35.423483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:35.774965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.140365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.498335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.844804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:37.374245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:33.903966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:34.311957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:34.724756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:35.461916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:35.814596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.181138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.539279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.883699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:37.415870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:33.952323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:34.355069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:35.117476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:35.500709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:35.855471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.220400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.577101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.920420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:37.455940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:33.997850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:34.399574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:35.160792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:35.536671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:35.892672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.261943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:36.615240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:10:37.088165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-08T15:10:39.051334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ageattendance_percentagediet_qualityexam_scoreexercise_frequencyextracurricular_participationgenderinternet_qualitymental_health_ratingnetflix_hoursparental_education_levelpart_time_jobsleep_hourssocial_media_hoursstudy_hours_per_day
age1.000-0.0230.000-0.010-0.0040.0790.0000.024-0.0460.0020.0000.0000.039-0.0120.002
attendance_percentage-0.0231.0000.0000.094-0.0090.0000.0200.052-0.008-0.0020.0320.0000.0120.0460.025
diet_quality0.0000.0001.0000.0550.0000.0510.0000.0000.0440.0000.0000.0000.0000.0000.047
exam_score-0.0100.0940.0551.0000.1500.0000.0000.0450.323-0.1650.0610.0000.123-0.1660.812
exercise_frequency-0.004-0.0090.0000.1501.0000.0500.0650.000-0.000-0.0080.0200.0490.019-0.034-0.038
extracurricular_participation0.0790.0000.0510.0000.0501.0000.0000.0000.0000.0000.0000.0000.0740.0870.000
gender0.0000.0200.0000.0000.0650.0001.0000.0350.0000.0000.0310.0000.0000.0000.037
internet_quality0.0240.0520.0000.0450.0000.0000.0351.0000.0500.0390.0000.0560.0000.0000.000
mental_health_rating-0.046-0.0080.0440.323-0.0000.0000.0000.0501.0000.0030.0430.066-0.005-0.004-0.009
netflix_hours0.002-0.0020.000-0.165-0.0080.0000.0000.0390.0031.0000.0000.000-0.0160.012-0.036
parental_education_level0.0000.0320.0000.0610.0200.0000.0310.0000.0430.0001.0000.0080.0000.0000.000
part_time_job0.0000.0000.0000.0000.0490.0000.0000.0560.0660.0000.0081.0000.0000.0000.051
sleep_hours0.0390.0120.0000.1230.0190.0740.0000.000-0.005-0.0160.0000.0001.0000.015-0.031
social_media_hours-0.0120.0460.000-0.166-0.0340.0870.0000.000-0.0040.0120.0000.0000.0151.0000.021
study_hours_per_day0.0020.0250.0470.812-0.0380.0000.0370.000-0.009-0.0360.0000.051-0.0310.0211.000

Missing values

2025-11-08T15:10:37.521180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-08T15:10:37.578033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

student_idagegenderstudy_hours_per_daysocial_media_hoursnetflix_hourspart_time_jobattendance_percentagesleep_hoursdiet_qualityexercise_frequencyparental_education_levelinternet_qualitymental_health_ratingextracurricular_participationexam_score
0S100023Female0.01.21.1No85.08.0Fair6MasterAverage8Yes56.2
1S100120Female6.92.82.3No97.34.6Good6High SchoolAverage8No100.0
2S100221Male1.43.11.3No94.88.0Poor1High SchoolPoor1No34.3
3S100323Female1.03.91.0No71.09.2Poor4MasterGood1Yes26.8
4S100419Female5.04.40.5No90.94.9Fair3MasterGood1No66.4
5S100524Male7.21.30.0No82.97.4Fair1MasterAverage4No100.0
6S100621Female5.61.51.4Yes85.86.5Good2MasterPoor4No89.8
7S100721Female4.31.02.0Yes77.74.6Fair0BachelorAverage8No72.6
8S100823Female4.42.21.7No100.07.1Good3BachelorGood1No78.9
9S100918Female4.83.11.3No95.47.5Good5BachelorGood10Yes100.0
student_idagegenderstudy_hours_per_daysocial_media_hoursnetflix_hourspart_time_jobattendance_percentagesleep_hoursdiet_qualityexercise_frequencyparental_education_levelinternet_qualitymental_health_ratingextracurricular_participationexam_score
990S199018Male3.23.51.7No91.76.5Good1MasterGood5No63.6
991S199120Male6.02.13.0No86.75.1Good2High SchoolGood3No85.3
992S199218Male3.50.01.9No96.86.4Fair3BachelorPoor3No71.8
993S199320Male3.82.11.0No89.05.2Good1High SchoolGood7No70.9
994S199420Female1.61.32.9No75.35.6Good0High SchoolAverage5No41.7
995S199521Female2.60.51.6No77.07.5Fair2High SchoolGood6Yes76.1
996S199617Female2.91.02.4Yes86.06.8Poor1High SchoolAverage6Yes65.9
997S199720Male3.02.61.3No61.96.5Good5BachelorGood9Yes64.4
998S199824Male5.44.11.1Yes100.07.6Fair0BachelorAverage1No69.7
999S199919Female4.32.91.9No89.47.1Good2BachelorAverage8No74.9